add the script

fsmt-make-tiny-model.py

@@ -0,0 +1,59 @@
+#!/usr/bin/env python
+# coding: utf-8
+# Copyright 2020 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This script creates a super tiny model that is useful inside tests, when we just want to test that
+# the machinery works, without needing to the check the quality of the outcomes.
+#
+# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
+# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
+# This gives ~3MB in total for all files.
+#
+# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
+#
+#
+# It will be used then as "stas/tiny-wmt19-en-de"
+
+# Build
+from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
+mname = "facebook/wmt19-en-de"
+tokenizer = FSMTTokenizer.from_pretrained(mname)
+# get the correct vocab sizes, etc. from the master model
+config = FSMTConfig.from_pretrained(mname)
+config.update(dict(
+    d_model=4,
+    encoder_layers=1, decoder_layers=1,
+    encoder_ffn_dim=4, decoder_ffn_dim=4,
+    encoder_attention_heads=1, decoder_attention_heads=1))
+
+tiny_model = FSMTForConditionalGeneration(config)
+print(f"num of params {tiny_model.num_parameters()}")
+
+# Test
+batch = tokenizer(["Making tiny model"], return_tensors="pt")
+outputs = tiny_model(**batch)
+
+print("test output:", len(outputs.logits[0]))
+
+# Save
+mname_tiny = "tiny-wmt19-en-de"
+tiny_model.half() # makes it smaller
+tiny_model.save_pretrained(mname_tiny)
+tokenizer.save_pretrained(mname_tiny)
+
+print(f"Generated {mname_tiny}")
+
+# Upload
+# transformers-cli upload tiny-wmt19-en-de